Tag Archives: Enegry Data Management

Optimising Energy Consumption in Humidification Plants #Textile #EnergyEfficiency

Humidification plants in Textile (spinning) consume almost 15%-18% of the total electricity consumption. Out of which nearly 80% to 90% goes to circulating “air” and is consumed by significant number of Fans (Industrial), hence even if the H plant has automation it at most impacts the 10% to 20% of the total electricity consumption, delivering gains of around ~1%.

Carefully playing around with the air flow during different time zones (day wise) could deliver gains of around 5%-7% at no or minimum cost (VFDs are installed across all Fans)

  1. Knowing when to “manipulate” air flow

Operators do intervene and manipulate the air flow during the day based on in room conditions etc, however it is something that is mostly “reactive” in nature.  If we can collect data sets like Heat Load, In Room Condition etc from different data acquisition systems, we would be able to predict exact hours when the air flow could be optimised and to what %, this allows the operator to plan the changes and be more scientific and data driven without having to indulge in any complexity.

2.  Automating the “manipulation”

For organisations that do not want to have this done manually, we have developed integrated logic for providing feedback to VFD on control of air flow factoring in room and outside conditions.

Want to give our algorithm a test? Visit www.boostenergyefficiency.com , register your request and our team will take it ahead from there!

Retrospective assessments before moving to predictive alerts #DataAnalytics #EnergyAnalytics

Greetings!

I was recently onsite for a data discovery exercise, unit has one of the largest single location manufacturing capacity in its sector. Of a lot of data sets we looked at, one of the interesting case that came in front of us was that of a vibration of a Fan, one that is very important in the entire process, lowering of the operational RPM could result in significant production loss.

We took past data sets and wanted to understand how the retrospective assessment is done by the team, as expected a lot of time went into fact finding and was dependent on a lot of people. Besides taking time, no one could point out exactly when the issue started building up and when would have been the right time to respond to it?

What did our algorithms (series of logics, no ML really) find out?

1.       Total of 1953 peaks happened, where in the rise in vibration % was such that if continued it could have mean an X% increase over 24 hours.

2.       1606 cases where the peaks where in consecutive points the vibration increased by 50% of X%, we have called them as Alerts. (In the current scheme of things alarm only goes when things are out of control)

3.       823 out of 1606 cases had consecutive alerts, in quite of a few of them 4-5 alerts came in successively. (Remember these alerts are not simply a>b “raise alarm”, it tracks the tendency and past pattern)

4.       There were 7 occasions (exact date and time pointed out) when plant had an unplanned shutdown (over 8 hours) and the problem could have been addressed. (Next time when that happens an maintenance team already has a ticket to address the issue)

5.       Algorithm automatically pointed out how maintenance activity in one of the cases could normalize the increase in vibration %, while in the other they couldn’t or perhaps no action was taken. (So if a ticket is marked resolved and technically the problem stays, the algorithms points out it close to real time)

6.       Because of last two tickets going un attended the unit lost out 7% production over a stretch of 5 weeks and had to wait for another unplanned shutdown to address the issue!

Point 1 to 6 happened even after people were looking at the screens 24X7! Time to have people taking actions and not looking at screens, real time monitoring is a thing of past, but to move to future the team needs to of adequate tools to do retrospective assessments and eventually go on to work on systems/tools that predict an anomaly building up well in advance!

Well that’s a real case study! Liked it? Would love to hear your views/thoughts!

Best Regards,

Umesh Bhutoria

Investing in Data Management Application? Have you considered these 3 points?

Greetings!

Over the last few months we have seen organisations investing or deciding to invest in suite of web and mobile applications to manage data and automate part of reporting process when it comes for scaled #EnergyEfficiency or #CleanerProduction focused programmes.  3 things that one must consider before deciding in selecting the right vendor:

1. Thought Leadership

Use of #AI or simply put series of logics to automate certain processes is evolving, there is a lot of noise when it comes to people talking about it. One must decide to work with partners that have worked on similar applications before and have had the habit of innovating in the domain.

2. It’s not about IT

Developing an application or designing a form is not the important part. What the system does and how it helps is important? Some of the potential benefits that must come from such a system is reduced project management costs, standardisation etc? So if your vendor has not delivered it before there are chances that it they might fall short again.

3. Business Model Innovation

Such applications have to evolve every day so that they can last for 4-5 years, hence it is important to consider innovative business models before embarking on the “product” development. Vendor with core interest in such applications is best suited as against to a conventional “Developer”

EnergyTech Ventures is an emerging company that has the largest portfolio in the #DataHub Space with it’s application in the #EnergyEfficiency and #CleanerProduction programmes being used by 100+ factories in 6+ countries. We have helped organisations reduce the operation costs by around 30% when it comes to data crunching, validation and reporting.

To know more about our work please visit us at www.entechventures.com 

Over Obsession with “Savings” will cost your organization dearly! #EnergyAnalytics #EnergyEfficiency

Over obsession with “Savings” starts to cost organisations dearly, anything that does not straight away give X% savings isn’t looked at with interest, potentially ignoring the fundamental value addition it brings in sustaining #EnergyEfficiency or #EnergyProductivity improvement.

Without doubt Savings either in the form energy consumption or cost reduction or a combination, is something that drives decision making process when it comes #EnergyEffciency investments.

To be frank we cannot blame industries entirely for it, i mean that is the way most of products, solutions around #EnergyEfficiency have been sold to them. People have spoken about #ROI, #Payback etc, seldom people talk about aspects like Performance Management, Automated M&V, Automated Reporting & Scientific Budgeting, aspects that have greater impact on organisational and cultural change when it comes to #EnergyEfficiency #EnergyProductivity management.

Some of the forward looking organisations have realized that having a myopic view on ways to improve #EnergyEfficiency #EnergyProductivity will cost them dearly in the longer run. They are looking at driving changes that have a long term vision and an integrated strategy, they are not looking at executing things in isolation. That is going to be the differentiator.

Your thoughts?

Umesh Bhutoria

How close are we to having a Central Energy Efficiency Data Repository?

For all of us who have been wanting to see an inclusive energy strategy backed by Data, Niti Aayog’s focus on Energy Data Management & decision to set up of an Energy Data Agency comes as a pleasant surprise.

What impact will National Energy Data Agency have on Industrial Energy Efficiency? Will this also lead to setting up a Central Energy Efficiency Data Repository?

In this short video i talk about my take on these questions!

Would be glad to have your thoughts/feedback on the same!

Cheers,

Umesh